Understanding Streaming Music Diffusion in a Semi-Closed Social Environment

Music social networks play a role in the diffusion of music. There are different ways a piece of music reaches people in a network: through the influence of social connections or via the discovery of external information, such as mass media, newspapers, etc. This empirical study uses over 10 months of user listening data from a music social network to examine the effects of external information on streaming music diffusion at the macro- and micro-levels. The data include weekly listening records for 557,554 users. Our results suggest that external information is a significant driver of increased streaming music diffusion, in comparison to in- network influences. We also found evidence of variation in the different influences, such as for a scale effect, the validity and type of information shared, and the impact of geolocation. These insights can be used to promote music and design personalized music recommendations

As empresas olham além de seus muros para inovar?

ABSTRACTThis paper analyses the influence of universities, research institutes, laboratories, consultancies, suppliers and clients on the innovation processes held at firms. These external actors tend to outline networks considered essential for learning and innovation processes. Changes in the systems of production during the last 30 years made information flows and knowledge diffusion stand out as core factors for competitiveness. The main hypothesis of this paper is that the innovative performance of Brazilian manufacturing firms is related to the levels of interaction that companies develop with such external actors. Analysis based on interviews with entrepreneurs and CEOs of 106 Brazilian companies – from the Research of Entrepreneurs Attitudes for Development and Innovation (PAEDI) – show a relationship between innovative attitude and interactional level: firms with higher levels of interaction with “external actors” tend to be the ones who area more innovative

LATTE: Application Oriented Social Network Embedding

In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node is represented as a feature vector. However, due to the detachment of embedding process with external tasks, the learned embedding results by most existing embedding models can be ineffective for application tasks with specific objectives, e.g., community detection or information diffusion. In this paper, we propose study the application oriented heterogeneous social network embedding problem. Significantly different from the existing works, besides the network structure preservation, the problem should also incorporate the objectives of external applications in the objective function. To resolve the problem, in this paper, we propose a novel network embedding framework, namely the "appLicAtion orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network structure can be applied to compute the node "diffusive proximity" scores, which capture both local and global network structures. Based on these computed scores, Latte learns the network representation feature vectors by extending the autoencoder model model to the heterogeneous network scenario, which can also effectively unite the objectives of network embedding and external application tasks. Extensive experiments have been done on real-world heterogeneous social network datasets, and the experimental results have demonstrated the outstanding performance of Latte in learning the representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl